Biases and Heuristics 1997

8/13/2019 Biases and Heuristics 1997

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EXECUTIVESUMMARY

DIFFERENCES BETWEEN

ENTREPRENEURS ANDM N GERS IN LARGE

ORGANIZATIONS: BIASES

ND HEURISTICS

IN STRATEGIC

DECISION MAKING

LOWELL W. BUSENITZUniversity of Houston

J Y

B.B RNEY

Ohio State University

The purpose o f his study was to further explore differences between entrepreneurs and managers in large organizations. However rather thanfocusing on previously examined individual differences this study examined differences in the decision-making processes used by entrepre-neurs and managers in large organizations. Build ing on nonrational de

cision-making model s from behavioral decision theory we asserted thatentrepreneurs are more susceptible to the use decision-making biases nd heuristics than are managers in large organizations.

To understand why entrepreneurs nd managers in large organizations m y vary in the extentto which they manifest biases and heuristics in their decision- making it is important to understandthe utility o f nonrational decision-making. Under conditions o f environmental uncertainty andcomplexity biases and heuristics can be an effective and efficient guide to decision-making. In suchsettings more comprehens ive and cautious decision-making is not possible nd biases and heuris-

Address correspondence to Lowell W. Busenitz College of Business Administration University ofHouston Houston TX 77204-6283.

The authors wish to thank Dale Rude and two anonyomous reviewers for their constructive commentson an earlier draft of the article.

Journal of Business Ventur ing 12 9-30 1997 Elsevier Science Inc.655 Avenue of the Americas New York NY 10010

0883-9026/97/ 17.00PH S0883-9026 96)OOOO3-1


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1 L.W. BUSENITZ AND J.B. BARNEY

tics may provide an effective way to approximate the appropriate decisions. The use o f heuristicshas also been found to be associated with innovativeness. Perhaps a critical difference between thesesets o f ndividuals is the extent to which they manifest biases and heuristics in their decision-making.We examined differences between entrepreneurs and managers in large organizations with respectto two biases and heuristics: overconfidence (overestimating the probability o f being right) andrepresentativeness (the tendency to overgeneralize from a few characteristics or observations).

In this study, entrepreneurs are those who have founded their own firms and are currentlyinvolved in the start-up process with the average time since founding o f 7 years. The analysis forthis study involved responses from 124 entrepreneurs. Managers are individuals with middle toupper level responsibilities with substantial oversight in large organizations. To be included in thisstudy, the managers had to oversee at least two functional areas (sample average was 4 55 functionalareas). Usable responses were received from 95 managers.

The results from the logistic regression analysis show strong suppor t for both hypotheses. Evenafter controlling for numerous factors, such as several traits and demographic factors, enduringsuppor t was found for the way entrepreneurs and managers in large organizations make decisions.

ur overconfidence and representativeness variables correctly categorized entrepreneurs and man

agers more than 70 o f he time. Thus, this research indicates that entrepreneurs do behave differently than do managers in large organizations and that these differences are substantial.

Practically, we speculate that without the use of biases and heuristics, many entrepreneurialdecisions would never be made. With entrepreneurial ventures in particular, the window o f opportunity would often be gone by the time all the necessary information became available for more rational decision-making. Additionally, successfully starting a new business usually involves overcoming multiple hurdles. Using biases and heuristics s simplifying mechanisms for dealing with thesemultiple problems may be crucial. To face such hurdles from a strict econometric approach wouldnot on ly postpone decisions, but would in all likelihood make them overwhelming. More specifically, overconfidence may be particularly beneficial in implementing a specific decision and persuading others to be enthusiastic about it s well.

The use o f biases and heuristics may also offer some help in explaining why entrepreneurssometimes make bad managers. Whereas the use o f cognitive biases may be beneficial in some circumstances, it can lead to major errors in others. Al though research has yet to establish performanceimplications, it is possible that the more extensive use o f heuristics in strategic decision-making maybe a great advantage during the start-up years. However, it may also lead to the demise o f a business

s a firm matures. 997 Elsevier Science Inc.

INTRODUCTION

Casual observation suggests that individuals who start their own organizations aresomehow different from those that work in large organizations. Entrepreneurs havebeen described as risk-takers and rugged individualists Begley and Boyd 1987;McGrath et al. 1992), as engaging in deviate social behavior Shapero 1975), and asbeing a breed apart Ginsberg and Buchholtz 1989). n contrast, managers in largeorganizations have been described as being risk averse Amihud and Lev 1981), adhering to broadly accepted norms of behavior Pettigrew 1973), and more professional andpredictable in their decision-making Barnard 968; Hofer and Schendel 1978).

These casual observations have not gone untested in the research literature. Unfortunately, most efforts to empirically describe differences between entrepreneurs andmanagers in large organizations have met with limited success Low and MacMillan1988). These results have led many researchers to abandon the search for individual

differences between entrepreneurs and managers in large organizations Gartner 1988)and to look for explanations of entrepreneurial phenomena elsewhere Aldrich and


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ENTREPRENEURS VS MANAGERS 11

Zimmer 1986). nd yet, the observation that these differences exist continues e.g., Bird1988; Ginsberg and Buchholtz 1989).

The purpose of this study is to further explore differences between entrepreneursand managers in large organizations. However, rather than focusing on previously ex

amined individual differences, this study examines differences in the decision-makingprocesses used by entrepreneurs and managers in large organizations. Building on nonrational decision-making models from behavioral decision theory Pitz and Sachs 1984;Stevenson et al. 1990), we argue that the use of bias and heuristics may explain significantportions of the variations in strategic decision-making Haley and Stumpf 1989). Morespecifically, we argue that entrepreneurs use biases and heuristics more extensively intheir strategic decision-making than do managers in large organizations. We examinedifferences between entrepreneurs and managers in large organizations with respectto two biases and heuristics: overconfidence and representativeness Tversky and Kahneman 1974; Hogarth 1987; Bazerman 1990).

PREVIOUS RESE RCH

Previous research on differences between entrepreneurs and managers in large organizations has generally examined psychological and personal/demographic differences.After a great deal of research e:g., McClelland 1961; Brockhaus 198 ; Schere 1982),it is now often concluded that most of the psychological differences between entrepreneurs and managers in large organizations are small or nonexistent Brockhaus andHorwitz 1986; Low and MacMillan 1988).

In the psychological differences literature, a wide variety of individual psychological attributes, including locus of control and risk-taking, has been shown not to vary

significantly between entrepreneurs and managers in large organizations Begley andBoyd 1987; Sexton and Bowman 1984). Some relatively small but consistent psychological differences have been documented such as need for achievement, tolerance for ambiguity, and need for conformity Begley and Boyd 1987; Miner et al. 1989). Despite thefact that very few studies have shown statistically significant differences between entrepreneurs and managers in large organizations in their risk-taking propensity Brockhaus1980, Low and MacMillan 1988), this individual psychological difference continues tobe discussed as an important variable for understanding entrepreneurial behavior e.g.,Stevenson and Gumpert 1985; Ray 1994).

Research focusing on personal/demographic differences between these types of individuals has also been met with limited success. Cooper and Dunkelberg 1987) concluded from their large sample that such differences between entrepreneurs and managers in large organizations are quite small and rarely systematic. Despite these findings,age, gender, and education are several personal/demographic differences that continueto receive attention in the entrepreneurship literature Robinson and Sexton 1994).

There have been three fairly distinct responses to the failure to discover systematicpsychological and personal/demographic differences between entrepreneurs and managers in large organizations. First, some have argued that this failure represents inadequate methodology Ginsberg and Buchholtz 1989). Unfortunately, the application ofmore sophisticated methods has done little to alter earlier findings.

Second, others have argued that the search for individual differences should be

abandoned, in favor of research that focuses on external causes of entrepreneurial behavior Acs and Audretsch 1987; Aldrich and Zimmer 1986; Gartner 1988; Shapero


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2 L.W. BUSENITZ AND J.B. B RNEY

1984). Unfortunately, this more externally oriented work has been unable to completelyabandon the individual differences approach. For example, many economists have argued that entrepreneurial behavior is caused by market imperfections that create anopportunity for entrepreneurs to earn above average economic wealth (Hebert and

Link 1988). While emphasizing external market forces in motivating entrepreneurialactivity, these models fail to explain why some individuals are able to perceive and exploit these opportunities, whereas others are not. This has led some economists to acknowledge that entrepreneurs have "special aptitudes" (Schumpeter 1934), "specialresources" (Schultz 1975), and unusual levels of "alertness" to economic opportunities(Kirzner 1973; Kaish and Gilad 1991). Unfortunately, the sources of these individualdifferences are left largely unexplained in economic models of entrepreneurship.

Finally, another group of scholars continues to assert tha t individual differencesexist between entrepreneurs and managers in large organizations. However, this groupgenerally suggests that previous research has examined the "wrong" individual differences (e.g., Robinson et al 1991). Rather then examining psychological and personaldemographic differences, this group of scholars suggests the importance of examiningdifferences in behavior between these sets of individual (Gartner et al. 1992). Researchis increasingly focusing on the more behavioral approach (e.g., Covin and Slevin 1991;Miller 1983; Manimala 1992; Zahra 1993). Robinson et al (1991) developed several attitudinal scales for predicting behavioral tendencies. This study is consistent with this lastgroup of scholars, particularly that of Manimala (1992) who identified 186 examplesof very specific heuristics used by entrepreneurs. Our intent is to better understand thedecision-making style of entrepreneurs. More specifically, we probe why entrepreneursand managers in large organizations may vary in the extent to which they manifest biasesand heuristics in their strategic decision-making. n this study, entrepreneurs are those

who have founded their own firms. Managers are individuals with middle to upper levelresponsibilities with substantial oversight in large organizations.

TH ORY

Biases Heuristics and Entrepreneurial Decision-Making: Previous Work

Since Simon's (1955) early work, organizational scholars have recognized that managerial decision-making often falls short of the purely rational model (e.g., Haley andStumpf 1989) Several factors that prevent purely rational decision-making have beencited, including: (1) the high costs of such decision-making efforts (Simon 1979), (2)information-processing limits of decision-makers (Abelson and Levi 1985), (3) differences in decision-making procedures adopted by managers (Shafer 1986), and (4) differences in the values of decision-makers (Payne et al 1992).

One of the most important classes of models that explain deviations from rationaldecision-making focuses on biases and heuristics (Kahneman et al. 1982; Schwenk 1988;Stevenson e t al. 1990). Biases and heuristics are decision rules, cognitive mechanisms,and subjective opinions people use to assist in making decisions. Frequently, the useof biases and heuristics yields acceptable solutions to problems for individuals in aneffective and efficient manner. n this study, the term "biases and heuristics" is usedto refer to these simplifying strategies that individuals use to make decisions, especially

in uncertain and complex conditions.Although most previous research has been conducted in laboratory conditions, a


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ENTREPRENEURS VS M N GERS 13

broad range of empirical findings suggests that most decision-makers apply biases andheuristics to simplify their decision-making most of the time (Bateman and Zeithaml1989; Jackson and Dutton 1988; Kahneman et al 1982; Zajac and Bazerman 1991) andthat inquiry into such behavior is critical for understanding strategic decision-making

(Bazerman 1990; Cowen 1991; Schwenk 1988). This research is also consistent with thisobservation. However, we acknowledge the possibility that all decision-makers may notbe subject to these biases and heuristics in their decision-making to the same degree.In support of this research direction, decision-makers have recently been found to havedifferent cognitive trails (Haley and Stumpf 1989; Stumpf and Dunbar 1991). Furthermore, Bazerman and Neale (1983, p 317) noted in their review that although individualsare generally affected by systematic deviations from rationality, some individuals appear to be more accurate in their interpersonal judgments or less influenced by the frameof the situation. The possibility that there may be differences in the extent to whichdecision-makers are subject to biases and heuristics suggests an interesting possibilityfor research on differences between entrepreneurs and managers in large organizations.Biases and heuristics may be particularly critical for explaining variations in strategicdecisions (Haley and Stumpf 1989). Perhaps a critical difference between these two setsof individuals is the extent to which they manifest biases and heuristics in their decision-making.

Differences in the se of ias and Heuristics

To understand why entrepreneurs and managers in large organizations may vary in theextent to which they manifest biases and heuristics in their decision-making, it is important to understand the utility of non-rational decision-making. Under conditions of envi

ronmental uncertainty and complexity, biases and heuristics can be an effective andefficient guide to decision-making (Pitz and Sachs 1984). In such settings, more comprehensive and cautious decision-making is not possible, and biases and heuristics may provide an effective way to approximate the appropriate decisions (Tversky and Kahneman1974; Haley and Stumpf 1989). Also, entrepreneurship has been characterized as an

enactment process where acting precedes thinking (Gar tner et al 1992; Weick 1979).In this sense, entrepreneurship is more a function of actions taken than some objectiveset of conditions.

ecision UncertaintyOn average, the level of uncertainty facing entrepreneurs in making decisions is greaterthan the level of uncertainty facing managers in large organizations in making decisions(Hambrick and Crozier 1985; Covin and Slevin 1989). t the very least, managers inlarge organizations usually have access to historical trends, past performance, and otherinformation that can help reduce the level of uncertainty they face in making decisions(Mintzberg 1973). Moreover, this information can reduce the level of uncertainty facingdecision-makers in large firms at relatively low costs (Thompson 1967). In this sense,managers in large organizations can more closely approximate the rational ideal in theirdecision-making.

Entrepreneurs, on the other hand, often have to make decisions where there are

no historical trends, no previous levels of performance, and little if any specific marketinformation (Miller and Friesen 1984). Just the decision to start a venture based on a


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new product or service involves making numerous decisions for which there is little or nohard information. Furthermore, the market's acceptance of the new product or servicealmost always contains a great deal of uncertainty. However, efforts by entrepreneursto reduce their uncertainty in decision-making are likely to be very costly and usually

not terribly effective. nd yet, decisions still need to be made if the venture is goingto be launched or to quickly move forward once the venture has started (Eisenhardt1989; Miller 1983; Wally and Baum 1994). A greater use of biases and heuristics facilitates a perceived sense of overall understanding and a sense that the rules ofthe gameare now understood. Thus, we argue that those who are more susceptible to the useof biases and heuristics in decision-making are the very ones who are most likely tobecome entrepreneurs. The more cautious decision-makers will tend to be attractedto larger organizations where more methodical information tends to be more readilyavailable. Entrepreneurial activities simply become too overwhelming to those who areless willing to generalize through the use of bias and heuristics.

Decision omplexityNot only are the decisions made by entrepreneurs, on average, made in a more uncertainenvironment then decisions made by managers in large organizations, the decision-making context facing entrepreneurs also tends to be more complex then that facing managers in large organizations (Covin and Slevin 1991; Gartner et al. 1992; Miller and Friesen1984). Whereas they may have some potential pitfalls, biases and heuristics are likely tohave more utility in these highly complex decision settings, compared with less complexdecision settings (Pitz and Sachs 1984; Tversky and Kahneman 1974).

Large organizations develop elaborate policies and procedures to aid managers in

their decision-making. Nelson and Winter (1982) call these decision-making practicesroutines and emphasize the ability of routines to simplify the decision-making com

plexity facing managers. In addition to these routines, large organizations adopt elaborate organizational charts that define areas of decision-making responsibility. These limits have the effect of reducing the complexity of the decision-making context facing afirm, thus enabling managers in large organizations not to rely on biases and heuristicsas much.

Entrepreneurs, on the o ther hand, usually have not developed the elaborate decision-making policies and procedures characteristic of large firms (Fredrickson and Iaquinto 1989). In this context, simplifying biases and heuristics may have a great dealof utility in enabling entrepreneurs to make decisions that exploit brief windows of opportunity (Tversky and Kahneman 1974; Hambrick and Crozier 1985; Stevenson andGumpert 1985). Furthermore, entrepreneurs are often opportunists, acting on an ideawith limited information (Gartner et al 1992). In doing so, they must convince numerousstakeholders of the credibility of the venture. Using base rate probabilities to justifytheir entrepreneurial ventures is usually meaningless.

t is important to recognize that we are not arguing that the decisions facing managers in large organizations are not uncertain or complex. Clearly, they often are. Rather,all we are arguing is that, on average, decisions made by entrepreneurs tend to be moreuncertain and more complex than the decisions made by managers in large organizations. Also, without some unsubstantiated enthusiasm, many ventures would never be

started or would quickly die following their start-up. This logic that biases and heuristicsmay have more utility for entrepreneurs is consistent with findings suggesting that entre-


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NTR PR N URS VS MANAGERS 15

preneurial posture and proactive ness are positively related to performance (Covin andSlevin 1989; Zahra 1993). Thus, those who make greater use of biases and heuristicsin their decision-making are more likely to find themselves in an entrepreneurial context. By doing so, they should be able to filter through more information and more effec

tively communicate to various stakeholders the legitimacy of the venture.

Specific Decision Making Differences

As previously suggested, a large number of biases and heuristics have been studied inthe nonrational decision-making literature (Hogarth 1987; Bazerman 1990). Fromamong all these biases and heuristics, we chose to examine differences between thesesets of individuals with reference to two biases and heuristics: overconfidence and representativeness. Overconfidence was chosen because it is considered somewhat characteristic of a number of other biases and heuristics identified in the literature (Kahnemanet al 1982). Representativeness is one of more widely used heuristics (Pitz and Sachs1984; Barnes 1984; Katz 1992) and is a good indicator of how quickly one is likely togeneralize from a single or limited number of experiences (Schwenk 1988).

verconfidence

First described by Oskamp (1965), overconfidence has been shown to exist in a widevariety of settings (Lichtenstein and Fischoff 1977; Bazerman 1990). Overconfidenceexists when decision-makers are overly optimistic in their initial assessment of a situation, and then are slow to incorporate additional information about a situation into theirassessmentbecause of their initial overconfidence (Fischhoff et al 1977; Alpert and

Raiffa 1982). For example, Fischhoff et al (1977) found that subjects who assigned oddsof 1000:1 of being correct were correct only 81 % of the time. Most decision-makersare overconfident in their estimation abilities and do not acknowledge the actual uncertainty that exists (Bazerman 1990). Furthermore, decision-makers are generally slowto incorporate additional information because of their confidence in their existing assumptions and opinions (Phillips and Wright 1977; Russo and Schoemaker 1989).

A priori, overconfidence seems likely to manifest itself in decisions made by entrepreneurs to a greater extent than in decision made by managers in large organizations.Overconfidence enables an entrepreneur to proceed with an idea before all the stepsto that specific venture are fully known. Even though enormous uncertainties exist inthis decision-making situation (e.g., is there a real economic opportunity to be exploited,how should that opportunity be exploited, how large is this opportunity, how will competitors react to this opportunity), a higher level of confidence is likely to encourage anentrepreneur to take action before it makes complete sense. Furthermore, being moreoptimistic than the data would suggest may serve to convince other potential stakeholders (such as investors, suppliers, customers, key employees) of the opportunity that affords them if they get in on the ground floor of the venture. Put differently, if entrepreneurs wait until all the facts are in to start convincing others that their venture isindeed legitimate, the opportunity they are seeking to exploit will most likely be goneby the time more complete data becomes available (Stevenson and Gumpert 1985).Mangers in large organizations, on the other hand, do not have to rely on their personal

confidence in making decisions to as great an extent. Rather, these mangers can rely ondecision-making tools and historical performance patterns to convince top management


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that their projects should have priority. These observations lead to the following hypothesis:

HI Entrepreneurs will manifest more overconfidence than will managers inlarge organizations.

Previous research provides some empirical support for Hl For example, Cooper et al.1988) found that entrepreneurs assigned a higher probability of success to their own

ventures compared with competing ventures. This is consistent with entrepreneurs having an unreasonably high level of confidence in their own decision-making ability. Also,Manimala 1992) found that entrepreneurs do manifest such tendencies in their decision-making. Although suggestive, this research only indicates that entrepreneurs domanifest overconfidence. However, as suggested earlier, most research on nonrationaldecision-making suggests that most decision-makers manifest various biases and heuristicsincluding overconfidenceto some extent. Unfortunately, this previous research doesnot examine whether or not entrepreneurs are more overconfident then managers inlarge organizations.

epresentativeness

Representativeness was first described by Tversky and Kahneman 1971) and is one ofthe most common of all decision-making biases and heuristics Hogarth 1987). Decisionmakers manifest this heuristic when they are willing to generalize about a person ora phenomenon based on only a few attributes of that person or only a few observationsof a specified phenomenon Nisbett and Ross 1980; Bazerman 1990). A wide variety of

problems hasbeen

developed to test representativeness with studies repeatedly showingthat subjects consistently ignore base rate information e.g., Bar-Hillel 1979; Tverskyand Kahneman 1974). Decision-makers consistently underestimate the error and unreliability inherent in small samples of data Payne et al. 1992).

The particular form of representativeness examined here is a willingness of decision-makers to generalize from small, nonrandom samples Tversky and Kahneman1971). The law of large numbers suggests that large random samples can be used tomake rigorous inferences about popUlation statistics. However, sometimes, decisionmakers are willing to make such inferences, not from large random samples, but fromsmall, nonrandom samples. The most common type of small nonrandom sample usedas a basis for generalization is of course, personal experience Kahneman et al. 1982).

Again, there is reason to believe that representativeness, and especially the willingness to generalize from small, nonrandom samples, is a decision-making short cut thatmay be particularly common in entrepreneurial settings Katz 1992). In such setting,large random samples to reliably estimate customer demand, production costs, andother key pieces of information are rarely available. Nor do most entrepreneurs havethe resources including the time) to engage in such systematic data collection. Indeed,such systematic data collection activities might prematurely reveal an entrepreneursproducts and technologies to competitors, thereby reducing the return potential of thoseproducts and technologies to competitors Porter 1980). In this setting, entrepreneursmust be willing to rely on small, nonrandom samplesin particular, their personal experi

ence with current and potential customersto guide their decision-making. Of course,managers in large organizations have to rely less on these nonrandom samples, and thus,


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on average, will be able to more closely approximate purely rational decision-making.These observations lead to the following hypothesis:

H : Entrepreneurs will manifest representativeness more extensively in their decision-making than will managers in large organizations.

As with overconfidence, Manimala's (1992) research indicates that entrepreneurs domanifest representativeness in their decision-making. However, Manimala fails to examine the extent to which entrepreneurs manifest this decision-making short cut in comparison with other decision-makers, including managers in large organizations.

METHO S

Samples

As implied by these hypotheses, samples from two populations were drawn: a sampleof entrepreneurs and a sample of managers in large organizations. Survey research wasused to collect the primary data.

Sample o EntrepreneursThe sales tax file of a state comptroller's office was used to identify potential entrepre-neurs, because it has been found to be a superior source for identifying new businesses(Busenitz and Murphy n Press). These files contain the name and address of the organi

zation, owner, organization type, SIC code, and date of first sale. A sample of firmsshowing a date of first sale within the past two years and having an SIC code in the2800,2900,3000,3500,3600,3700, and 3800 categories was selected. These SIC categories include the manufacturing of plastics, electronics, and instruments. A priori, it wasthought that these categories would represent a higher percentage of newly emergingfirms, because they represent more dynamic industries. This procedure resulted in asample of 573 firms. A mail questionnaire was developed and sent to the identified sample, and 176 responses were received for a 3 response rate.

Because, historically, identifying entrepreneurs has been somewhat problematicGartner 1988), we wanted to be more precise in our operationalization. Our operation

alization consisted of two dimensions. First, respondents had to have been a founderof the identified firm. Being responsible for an independent start-up is widely used asa distinguishing feature of entrepreneurship (e.g., Begley and Boyd 1987; Cooper etal. 1988; Miner et al. 1989) and thus was used here as a prerequisite for inclusion inthe sample for this study. With the second dimension, subjects had to be currently involved in the start-up process. This was operationalized by requiring subjects to havestarted their venture within the last two years and/or currently planning on starting another venture within the next five years. These restrictions resulted in 124 usable responses. The average time since founding for the entrepreneurs included in this samplewas 1.7 years. To test for a biased response, nonrespondents were compared with respondents based on the two-digit SIC categories identified previously. The results from

the chi-square test suggested that the usable response was not biased (5) 1.782;p = .878).


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Sample o Managers in arge Organizations

In this study, managers in large organizations were defined as individuals who have responsibility for at least two functional areas (such as market ing, finance, personnel, research and development, and manufacturing) and work for publicly owned organizations with more than 10,000 employees. These managers are often referred to asdivisional managers or general managers because they oversee multiple functional areas(e.g., marketing, research and development, accounting, manufacturing). Contac t wasmade with three organizations, and two agreed to participate in the study. Data collection was coord inated through the human resource departments of the respective organizations with a company cover letter attached. To be included in this sample, the managers had to oversee at least two functional areas (sample average was 4.55 functionalareas). A usable response rate of 54 % was received. The SIC for the managers includedin this sample came from the 1300, 3400, 3500, 3600, and 3800. 1 The results of the chisquare test between usable responses and nonrespondents again suggest that the re

sponse was not biased X2

(4) = 3.973; p = .59).

Measures

The central goal of this study was to measure the use of biases and heuristics as a partof the decision-making style of entrepreneurs and managers in large organizations.Thus, some diversity of decision problems was sought to give a better representation ofthe decision style in a specific context of these two groups of strategic decision-makers.

OverconfidenceTo measure overconfidence, the procedure used in the widely cited studies conductedby Fischhoff et al. (1977) and Lichtenstein and Fischhoff (1977) was replicated. A seriesof five questions based on death rates from various diseases and accidents in the UnitedStates was developed. All items were dichotomous in nature with the general form of

Which cause of death is more frequent in the United States? A. Cancer of all types, B.Heart disease. One of the two choices is correct based on the most recent vital statisticsreport prepared by the National Center for Health Statistics. Subjects were asked tomake two responses to each item. First, they were to choose one of the two alternativesas their best guess of the correct alternative. Second, they indicated, on a provided scale

ranging from 50% to 100% the level of confidence they had in their answer. In the instructions, they were told that 50% would indicate that their answer was a total guess,whereas 70% would indicate that they thought they had seven chances in 10 of beingcorrect. A response of 100% would indicate that they were totally confident that theirchoice was right. Again, in taking our cues from the earlier work of Lichtenstein andFischhoff (1977), all level of confidence responses were grouped into one of six probability categories: 0.50-0.59,0.60-0.69,0.70-0.79,0.80-0.89,0.90-0.99, and 1.00 for analy-

Not only do these industries largely coincide with those from which our entrepreneurs came, but theyare also industries characterized by high levels of research and development. Also, both organizations wesampled in these industries claimed that they attempted to recruit intrapreneur ial managers into their organizations. The choice of these firms creates a conservative test of our individual differences hypotheses sinceentrepreneurial type individuals tend to be attracted to these industries.


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sis purposes. Those probability responses that were in the 0.50-0.59 range were codedas 0.50, 0.60-0.69 responses as 0.60, etc.

Two primary measures were computed from this information. The first is whatFischhoff et al. (1977) referred to as the group calibration curve. This curve reveals how

closely the designated probability score for each category (0.50, 0.60, etc.) and for eachgroup (in this case entrepreneurs versus managers in large organizations) approximatesthe perfectly calibrated curve. For example, for all responses in the 0.70 category, perfectcalibration would be reflected in correct responses to the dichotomous part of the question seven out of 10 times. f all the responses in the 0.70 category were correct only60% of the time, one would conclude that the designated group was overconfident inthat category.

For the purpose of statistical analyses on each observation, a second score was computed. Again, following the lead of Fischhoff et al (1977), this was done by noting themean probability response across all items for each subject and the percentage of itemsfor which the correct alternative was selected. The difference between these two scoresthen becomes a measure of over- or underconfidence (a positive score indicates overconfidence, whereas a negative score indicates underconfidence). For example, a respondent who answered 0.50, 0.60, 0.70, 0.70, and 0.90 and gave the correct answer threeout of five times would receive an overconfidence score of 0.08 (mean of 0.68 minus 0.60).

epresentativeness

To measure the representativeness, we followed the approach used by Fong et al (1986)and Fong and Nisbett (1991) in which subjects were given scenarios representing various

types of real-to-life strategic decisions. As in the scenarios developed by Fong and colleagues, both of our scenario problems portrayed a strategic decision pitting two alternatives against one another. One alternative was based on quantitative/statistical information, whereas the other was based on heuristic reasoning. Problem 1 involved thepurchase of a major piece of equipment, whereas problem 2 depicted an automationupdate decision (see Appendix for these two problems). Subjects were told to decidebetween the two alternatives for each problem and then to describe their reasoning forreaching the designated decision. Coders then analyzed these responses to determinewhether heuristic type reasoning was used by the respondents to answer these scenarios.

The coding schema used to analyze the responses also closely paralleled that ofFong et al (1986) and Fong and Nisbett (1991). A code of I was given for responsesthat contained no mention of statistical reasoning, but relied instead on subjective opinions or simple rules of thumb. Examples of this form of reasoning included referenceto personal experience or simple decision rules like buy American and personalexperience. A code of 0 was given for responses that contained some form of statistical reasoning, including references to variability or sample size. An additional categorywas added because some responses from the subjects of this study were uncategorizablebased on the Fong et al (1986) criteria. These 17 uncategorizable responses were omitted from subsequent analyses.

After some initial training, all responses were coded blind to conditions accordingto these criteria by two individuals (one of the authors and a graduate student). There

was exact agreement between coder 1 and coder 2 84 % of the time across the two problems. n cases where disagreement existed, the evaluation of a third coder (another grad-


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2 L.W. BUSENITZ AND 1.B. BARNEY

uate student) was used to resolve the disparity. These results were then summed acrossthe two problems to create a single three-category variable (0-2). A 0 indicated thatan individual used statistical reasoning across both problems, whereas a 2 indicatedthat only heuristic reasoning was used.

ontrol Variables

Although previous research on differences between entrepreneurs and managers inlarge organizations has generated mixed results, we chose to include various measuresof these variables. Also, a measure of economic alertness has been included, becauseit has been used to help explain entrepreneurial activity and could impact decisionmaking.

Risk Taking nd Conformity

The psychological trait of risk-taking propensity was included because risk is so commonly associated with entrepreneurial activity. Conformity, although not studied as frequently as risk-taking, has received some consistent empirical support. These two traitswere assessed by using the Jackson Personality Inventory (Jackson 1976). Jackson(1977) reported reliability coefficients of 0.81-0.84 for risk-taking and 0.81-0.82 for conformity in terms of scale homogeneity and test-retest stability. Sexton and Bowman(1984) reduced this risk-taking scale to eight items to better accommodate survey research. The utilization of this eight-item risk-taking scale yielded a Kuder-Richardson-20 (KR-20) reliability coefficient of 0.77 in this study. A KR-20 of 0.70 was obtainedfor the conformity factor.

Personal/Demographic Characteristics

Age and education information was also collected, since entrepreneurship has been examined from this perspective. Additionally, it is conceivable that age and/or greateramounts of education may impact the use of biases and heuristics. Age was simply measured by asking for their birth year. Education was measured using a five-point scaleranging from high school or less to a graduate degree.

Economic FactorsSome have argued that economic concerns are what encourage entrepreneurial activity.

n the context of this study, factors involving risk and profit concerns and alertness toeconomic opportunities may impact entrepreneurial decision-making. Kaish and Gilad(1991) recently developed several scales to measure these factors. However, due to lowreliability estimates for the economic risk cue and economic profits scales received inthis study (0.47 and 0 57 respectively), they were omitted from further analyses. Kaishand Gilad (1991) also developed measures of alertness to economic opportunities(Kirzner 1973). The nonverbal search or reading alertness for economic opportunitygenerated a Cronbach's alpha reliability rating of 0 81 in this study. A second factor

measuring open thinking about new ideas received a reliability rating of 0.52 in thisstudy and thus was dropped from further analysis.


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RESULTS

Table 1 presents the means, standard deviations, and correlation coefficients amongall variables. These correlations show that the overconfidence and representativenessvariables are related to one 's vocation (entrepreneurs versus managers in large organizations), indicating preliminary support for both hypotheses.

Fur ther bivariate analysis was conducted with the overconfidence variable. The calibration curve shown in Figure 1 represents the aggregated results of the number ofcorrect responses in each category (50 , 60 , 100 ) divided by the total number ofresponses in each category. For example, if all the responses in the 0.70 category givenby managers were correct seven out of 10 times, then one would conclude that as agroup they tend to be perfectly calibrated (at least in that category). The charting ofthese two curves in Figure 1 indicates that entrepreneurs are overconfident in theirchoices in five out of the six probability categories, whereas managers were overconfident only three out of the six categories. Additionally, entrepreneurs were more over

confident than managers in large organizations in each of the categories except in the0.8 range probability where they were very nearly identical.

Further analysis was conducted with the overconfidence and representativenessvariables. Because the dependent variable in this model is dichotomous, logistic regression was used as the primary test a f H l and H2 (Aldrich and Nelson 1984). n this study,the dependent variable represented the entrepreneur versus manager in a large organization (coded 2 and I respectively).

Given the coding of the dependent variable, the coefficients for the overconfidenceand representativeness variables should be positive and significant. Table 2 presentsthe results of the logistic regression analysis. Model 1 tested the overconfidence and

representativeness variables by themselves. Both variables are significant and in the expected direction. These two variables by themselves correctly predicted entrepreneurversus manager more than 70 of the time.

Further examination of Table 1 suggests little collinearity among independent variables. However, several moderate intercorrelations involving control variables suggestthat including these variables in an analysis would be important. The results of this analysis are shown in model 2 of Table 2. Of the control variables, education, conformity,and alertness remain statistically significant. The risk-taking and age variables were nonsignificant. Overall, these results support the emerging consensus that psychological,personal/demographic, and broader social and economic factors have a limited ability to

TABLE Means, Standard Deviations, and Correlations

Variable Mean SD 1 2 3 4 5 6 7

1. Entrepreneur/Manager 1.6 0.492. Representativenss 1.14 0.78 0 61h

3. Overconfidence .17 0.17 0.20 b -0.054. Risk-taking 5.2 2.38 0.02 -0 .04 -0.045. Conformity 2.4 1.86 0.3Gb -0.07 -0.01 -.28 b

6. Education 3.32 1.27 0.62b -0.35 b -0.11 0.06 0.027. Age 44.55 9.7 -0.07 -0.07 0.03 -0.01 -0.11 0.018. Alertness 5.45 1.79 0.09 -0.04 -0.11 0 21b -0 .19 ' 0.09 0.17

p .05.b f .01.


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1.0

.9

.8

.7

.6--

.5

.4

nr .5

UnderconfidenceDomain

.6 .7 .8

OverconfidenceDomain

.9

Probability Response

FIGURE 1 Calibration curves for entrepreneurs and managers.

1 0

distinguish entrepreneurs from managers in large organizations (the hit ratio reported inTable 2 only improved 9 with the inclusion of the control variables). More importan tlyfor this study, continuing support was found for HI and H2. Even after controlling forpreviously examined factors, the overconfidence and representativeness measures remain statistically significant and help distinguish between entrepreneurs and managersin large organizations.

To ensure that an industry effect was not confounding the results, a subanalysiswas conducted. Based on the two-digit SIC classification, eight different industries wererepresented by these two samples. However, 62 of the entrepreneurs and 86 of themanagers in large organizations came from three industries (3500, 3600, and 3800). Asthese three industries are closely related (industrial and commercial machinery and

computer equipment; electronic equipment and components; and measuring, analyzing,and controlling instruments) and they represent the majority of the two samples, the


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TABLE 2 Results of Logistic Regression Analysis

Independent Variables

InterceptRisk-TakingConformityEducationAgeAlertnessRepresentativenessOverconfidencePseudo-R 2

Model X 2

dfHit ratio ( )

p .05.h P .01.p .001.

Modell

ParameterEstimate

2.07'

1.6'68

0.2154.43'

19870

30.64

36.498.36

ParameterEstimate

6.31'-0.007-0.39

-1.09'0.D3

0.341.56'2.760.37

108.5'185

79'

Model 2

17.60.005

11.6431.76

1.676.61

22.096.21

full logistic regression model i ~ n t i f i eearlier was again tested with this subsample.Overconfidence and representativeness still remain significant at the same levels as withthe full samples reported previously. The only significant change was with the alertnessvariable. With the full samples reported in Table 2 it was not quite significant at the.05 level, whereas with the subsample, it did turn significant.

DISCUSSION

These results have several important implications, both for research and for practice. These implications are discussed below.

Theoretical Implications

Common experience suggests that entrepreneurs and managers in large organizationsare, somehow, different from each other. t has been somewhat disconcerting that mostacademic efforts at discovering the sources of these experienced differences have metwith limited success. This study presents empirical evidence suggesting that entrepreneurs do behave differently than managers in large organizations and that these differences are substantial. By applying the theory of biases and heuristics, this study hasshown that entrepreneurs and managers in large organizations think differently.

This field study has important implications for future work on biases and heuristics.In particular, most previous work has focused on understanding whether specific biasesand heuristics exist and the conditions that affect their usage (Abelson and Levi 1985;Stevenson et al. 1990). This research accepts this general premise and moves to anotherquestion: Do individuals vary in the extent to which biases and heuristics operate intheir decision-making? The results of this study suggest that the extent to which deci

sion-makers deviate from the strict econometric approach may not be a constant andthat different individuals may utilize biases and heuristics to different degrees.


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Of course, this research only examines two biases and heuristics: overconfidenceand representativeness. Other biases and heuristics already identified in the literaturemay also differentiate between entrepreneurs and managers in large organizations. Future research will need to examine other biases and heuristics to more completely ex

plain the differences between entrepreneurs and managers in large organizations. Also,future research will need to refine current measures of biases and heuristics, to examinenot only if these phenomena exist, but also the extent to which they vary among individuals and organizations. Finally, additional samples of entrepreneurs and managers inlarge organizations should be examined to ensure the generalizability of the findingsreported here.

n addition to extending this research to examine other kinds of biases and heuristics, future work will need to examine whether the use of biases and heuristics in strategicdecision-making remains stable over time. Some have argued that biases and heuristicsare often applied in an unconscious manner Tversky and Kahneman 1981) and thusare relatively immune from change or modification. Alternatively, others have reasonedthat decision biases can be corrected through training e.g., Russo and Schoemaker

989; Fong and Nisbett 1991). f the use of biases and heuristics are stable over time,then it would follow that those who are uncomfortable with heuristic-based decisionmaking, on average, will be attracted and selected into larger firms where this type ofdecision-making is less frequently required. On the other hand, those who are comfortable with creating and relying on these decision-making short cuts are likely to be attracted to entrepreneurial settings where these decision skills are best utilized Schneider 1987; Haley and Stumpf 1989). n assumption future research could address iswhether individuals with different decision preferences will naturally and efficiently select into organizational contexts where those preferences are valued and accepted, i.e.,into entrepreneurial firms or larger organizations. Future research should also exploreif the use of biases and heuristics effectively helps with career placement decisions.

This research on biases and heuristics also potentially helps resolve some counterintuitive conclusions from previous work on risk. Few themes are as synonymous withentrepreneurship as risk, and yet, remain so clouded. Entrepreneurs clearly accepthigher levels of risk in their careers and business decisions than managers in large organizations Bird 1989), yet empirical evidence surrounding this phenomenon is diverseand often weak. For example, many psychological-based studies have shown that therisk-taking propensity of entrepreneurs is ot greater than that of managers in largeorganizations Brockhaus 1980; Low and MacMillan 1988). Thus, most academicians

hold tha t entrepreneurs do not differ substantially in their risk-taking propensity e.g.,Ray 1994). This conclusion is widely held even though it is clear that entrepreneurs aregenerally involved in starting ventures that are more likely to fail than succeed. Thiscontradiction is frequently resolved by characterizing entrepreneurs as risk acceptersversus risk seekers or risk averse). Thus, the focus has shifted toward understand

ing how to manage the risk inherent in an entrepreneurial opportunity Bird 1989;Ray 1994).

For understanding entrepreneurial behavior, the issue may not be one of risk propensity or the sensitivity to probability estimates of possible outcomes, but rather howentrepreneurs think about the decisions they make surrounding the business opportuni

ties they undertake Ray 1994). t may be that entrepreneurs are more susceptible tothe use of biases and heuristics and are likely to perceive less risk in a given decision


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ENTREPRENEURS V MANAGERS 5

situation than are managers in large organizations in the same situation. By being morewilling to generalize from limited experience (representativeness) and by feeling overconfident that they will be able to master the major obstacles, entrepreneurs may conclude that a situation is simply less risky than would managers in large organizations.

Thus, it may not be the differences in risk propensity that distinguish entrepreneursfrom managers in large organizations, but the ways they perceive and think about risk.

Finally, these observations have a potentially important impact in analyzing therelationship between decision-making and performance. Tversky and Kahneman(1974) noted that the use of heuristics may actually improve decisions by noting thatthey are quite useful (p. 1124); that they are valuable estimation procedure[s] (p.1128) and that they are highly economical and usually effective (p. 1131). Nonetheless, such claims are typically followed by warnings that heuristics may also lead tosevere and systematic errors (p. 1131). Rarely if ever do Tversky and Kahneman giveexamples of heuristics working well. However, if different individuals and organizationsare cognitively biased in different ways, then they may make strategic choices in fundamentally different ways (Stumpf and Dunbar 1991). f hese cognitive biases are difficultto change, they may represent sources of sustained differences among individuals andfirms. Such differences, in the field of strategic management, have been shown to besources of sustained competitive advantage and sustained competitive disadvantage(Barney 1991). By recognizing that strategic decisions can be nonrational in differentways, this research points to a possible connection between cognitive theories of decision-making, strategic management, and firm performance. Of course, additional research will need to be conducted to examine these potential linkages.

Practical mplicationsThere are several practical implications that emerge from this study as well. We suspectthat without these biases and heuristics, many decisions would never be made. In entrepreneurial ventures, in particular, the window of opportunity would often be gone by thetime all the necessary information became available for more rational decision-making.Additionally, successfully starting a new business usually involves overcoming multiplehurdles. Using biases and heuristics as simplifying mechanisms for dealing with thesemUltiple problems may be crucial. To face such hurdles from a strict econometric approach would not only postpone decisions, but would in all likelihood make them overwhelming. More specifically, a greater degree of overconfidence may be particularlybeneficial in implementing a specific decision and persuading others to be enthusiasticabout it as well (Russo and Schoemaker 1989).

As an illustration of the possible benefits of using biases and heuristics in decisionmaking, consider the following. One entrepreneur from our sample wrote to us becausehe took issue with the quantitative emphasis of our study. Overall, he rejected this systematic approach to decision-making. He stated:

You see, people who are engaged in businesses such as mine are rarely influenced by surveys because they don' t place any stock in them. Survey reports, in general, are most highly prized by those individuals who lack sufficient knowledge ofa matter in which they are required to make a decision. t is my considered opinion

that those individuals are not going to be found successfully engaging in entrepreneurial businesses.


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The use of biases and heuristics may also offer some help in explaining why entrepreneurs sometimes make bad managers Schell 1991). Whereas the use of cognitive biasesmay be beneficial in some circumstances, it can lead to major errors in others Tverskyand Kahneman 1974). Although research has yet to establish performance implications,

it is possible that more extensive use of heuristics in strategic decision-making may bea great advantage during the start-up years. However, it may also lead to the demiseof a business as a firm matures.

Implications may also exist for managers in large organizations in how they managethe entrepreneurial individuals in their firms. I t would appear that a fundamental decision needs to be made as to whether such individuals should be accommodated withinthe firm or released to go and start their own firm. As large organizations tend to becharacterized by more methodical decision-making, such environments can be very stifling for those more comfortable with biased and heuristic reasoning. Thus, if an organization values such individuals, it is important to find organizational contexts for lettingthese individuals make their contributions. More specifically, it has been suggested thatsuch individuals may be particularly good with problem solving and new product development Bazerman 1990; Russo and Schoemaker 1989). A second alternative is to decide that such individuals simply will not help an organization long-term. I t may be better to help these individuals to start thei r own firms, where their biased decision-makingapproach is likely to be more beneficial.

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PPENDIX

Problem 1: Equipmeut Purchase Decision

Mr. Johnson is about to invest in a new machine and has narrowed his options to Ma

chine A, whichis

made in the United States or Machine B, whichis

made overseas.Both machines are equally capable of performing the same function. In considering thisdecision, Mr. Johnson said to his friend, You know, it seems that every time I buy apiece of equipment made by a foreign manufacturer, it breaks down in the first monthof use.

After further discussion, Mr. Johnson's friend remembers a recent industrial reportthat gives a significantly higher ranking to Machine B (the one made overseas) thanto Machine A. This report bases its recommendation on extensive testing as well as onfeedback from dozens of users. f you were in Mr. Johnson's position, which machinewould you purchase? Why?

Problem 2: utomation Update Decision

The president is urging the board of directors to accept the purchase of a state-of-theart computerized machine that would fundamentally change their operations. After describing the capability of this machine, the president cites a recent nationwide studywhich examined 12 businesses making similar upgrades. Results indicated that at least85% showed a sizable increase in productivity. In a parallel control group of firms notmaking the upgrade, about half as many firms (40%) showed a sizable increase in productivity. Based on this study, the president concludes that the computerized machineneeds to be purchased.

Oneof the directors now takes the floor giving two reasons why computerizedequipment is not the real reason for increased productivity. First, the managers of busi

nesses that make such changes are likely to be more energetic and adventurous, thuscreating an environment for superior performance. Second, any change is likely to leadto superior performance because of the increased interest and commitment on the partof management.

f you were participating in such a decision, whose line of reasoning (president ordirector) would you be more likely to accept? Why?

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